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Record W2022096797 · doi:10.4141/cjps07019

Sensitivity of field populations of <i>Ascochyta rabiei</i> to chlorothalonil, mancozeb and pyraclostrobin fungicides and effect of strobilurin fungicides on the progress of ascochyta blight of chickpea

2007· article· en· W2022096797 on OpenAlexfundvenueno aff
K. F. Chang, Helal Uddin Ahmed, Sheau‐Fang Hwang, B. D. Gossen, Stephen E. Strelkov, S. F. Blade, G. D. Turnbull

Bibliographic record

VenueCanadian Journal of Plant Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsnot available
FundersAlberta Pulse Growers Commission
KeywordsAscochytaFungicideChlorothalonilAzoxystrobinBlightMancozebStrobilurinBiologyAgronomyHorticulture

Abstract

fetched live from OpenAlex

Chickpea production faces a major challenge from ascochyta blight (Ascochyta rabiei), a devastating disease that can cause total crop loss. To assess the effect of repeated fungicide application on disease progress, strobilurin fungicides, primarily alternating pyraclostrobin and azoxystrobin treatments, were applied up to five times per year in each of 2 yr. A single application or two early applications reduced blight severity. A third application resulted in additional benefits in 1 of 2 yr, but additional applications did not reduce severity further. To monitor for fungicide tolerance in populations of A. rabiei, 66 single- spore isolates were collected and grown on growth media amended with chlorothalonil, mancozeb, or pyraclostrobin. Insensitivity to one or more of the fungicides was detected in 49 (74%) of the isolates. Based on the effect on conidial germination, insensitivity to pyraclostrobin or chlorothalonil was observed in 26 of 37 isolates (70%). Repeated fungicide application may be selecting for insensitive isolates of the pathogen; fungicide application should be combined with cultural measures to control ascochyta blight. Key words: Fungicide insensitivity, Ascochyta rabiei

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.209
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations50
Published2007
Admission routes2
Has abstractyes

Explore more

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